ICG-liver test versus new biomarkers as prognostic markers for prolonged length of stay in critically ill patients - a prospective study of accuracy for prediction of length of stay in the ICU

Zöller, B; Spanaus, Katharina; Gerster, Rahel; Fasshauer, Mario; Stehberger, Paul A.; Klinzing, Stephanie; Vergopoulos, Athanasios; Eckardstein, Arnold von; Béchir, Markus

doi:10.1186/s13613-014-0019-7

Cited by 14 publications

(14 citation statements)

References 24 publications

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“…Patients with markedly decreased PDR-values (<8%) did not respond well to the extracorporeal granulocyte treatment. The PDR of indocyanine green provides information for the assessment of mortality and morbidity in critically ill patients, in patients with severe sepsis, and in patients underlying cardiac surgery [ 47 – 50 ]. Limitations of the LiMON-System are the nonexistent discrimination between liver function and perfusion of the splanchnic tract and false data in cases of hyperdynamic sepsis and hyperbilirubinaemia [ 51 , 52 ].…”

Section: Discussionmentioning

confidence: 99%

“…These factors are known to impair the cell functions and viability in HepG2/C3A cells [ 53 – 56 ]. Proinflammatory cytokines, for instance, are known to cause a dysfunction of mitochondria [ 50 ], to downregulate albumin synthesis [ 54 ], and to diminish function of some P450 cytochromes like CYP 1A2, CYP 2E1, and CYP 7A1 [ 55 , 56 ].…”

Section: Discussionmentioning

confidence: 99%

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Bioartificial Therapy of Sepsis: Changes of Norepinephrine-Dosage in Patients and Influence on Dynamic and Cell Based Liver Tests during Extracorporeal Treatments

Sauer

Altrichter

Haubner

et al. 2016

BioMed Research International

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Purpose. Granulocyte transfusions have been used to treat immune cell dysfunction in sepsis. A granulocyte bioreactor for the extracorporeal treatment of sepsis was tested in a prospective clinical study focusing on the dosage of norepinephrine in patients and influence on dynamic and cell based liver tests during extracorporeal therapies. Methods and Patients. Ten patients with severe sepsis were treated twice within 72 h with the system containing granulocytes from healthy donors. Survival, physiologic parameters, extended hemodynamic measurement, and the indocyanine green plasma disappearance rate (PDR) were monitored. Plasma of patients before and after extracorporeal treatments were tested with a cell based biosensor for analysis of hepatotoxicity. Results. The observed mortality rate was 50% during stay in hospital. During the treatments, the norepinephrine-dosage could be significantly reduced while mean arterial pressure was stable. In the cell based analysis of hepatotoxicity, the viability and function of sensor-cells increased significantly during extracorporeal treatment in all patients and the PDR-values increased significantly between day 1 and day 7 only in survivors. Conclusion. The extracorporeal treatment with donor granulocytes showed promising effects on dosage of norepinephrine in patients, liver cell function, and viability in a cell based biosensor. Further studies with this approach are encouraged.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Bioartificial Therapy of Sepsis: Changes of Norepinephrine-Dosage in Patients and Influence on Dynamic and Cell Based Liver Tests during Extracorporeal Treatments

Sauer

Altrichter

Haubner

et al. 2016

BioMed Research International

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“…Patients with pLOS may have serious medical situations and thus need a longer hospital stay. We adopted this pLOS definition in our study by referring to existing studies [44][45][46] and consulting with experienced clinicians. The pLOS prediction models developed in our study aimed to assist physicians in identifying patients at a high pLOS risk and thus to provide early and timely interventions for these high-risk patients.…”

Section: Outcome and Predictor Variablesmentioning

confidence: 99%

“…In this study, pLOS was defined as an LOS longer than the average LOS by referring to existing studies [44][45][46] and consulting with experienced clinicians. In the literature, there is no consensus on the definition of pLOS for general patients or PD-treated patients.…”

Section: Definition Of Plosmentioning

confidence: 99%

“…In the literature, there is no consensus on the definition of pLOS for general patients or PD-treated patients. Existing studies have defined pLOS as an LOS longer than the average LOS [44][45][46], longer than the median LOS [68], or longer than a specific LOS according to experiences [69]. After consulting with experienced clinicians, we know that the average LOS is a more important metric for PD-treated patients, and it is also a more commonly used metric in assessing medical efficiency around the world.…”

Section: Definition Of Plosmentioning

confidence: 99%

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Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis–Treated Patients Using Stacked Generalization: Model Development and Validation Study

Kong¹,

Wu²,

Chu³

et al. 2021

JMIR Med Inform

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Background The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients. Objective This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data. Methods Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model. Results The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy. Conclusions This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.

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Leveraging electronic health record data to inform hospital resource management

Ferrão

Oliveira

Gärtner

et al. 2021

Health Care Manag Sci

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Early identification of resource needs is instrumental in promoting efficient hospital resource management. Hospital information systems, and electronic health records (EHR) in particular, collect valuable demographic and clinical patient data from the moment patients are admitted, which can help predict expected resource needs in early stages of patient episodes. To this end, this article proposes a data mining methodology to systematically obtain predictions for relevant managerial variables by leveraging structured EHR data. Specifically, these managerial variables are: i) Diagnosis categories, ii) procedure codes, iii) diagnosis-related groups (DRGs), iv) outlier episodes and v) length of stay (LOS). The proposed methodology approaches the problem in four stages: Feature set construction, feature selection, prediction

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ICG-liver test versus new biomarkers as prognostic markers for prolonged length of stay in critically ill patients - a prospective study of accuracy for prediction of length of stay in the ICU

Cited by 14 publications

References 24 publications

Bioartificial Therapy of Sepsis: Changes of Norepinephrine-Dosage in Patients and Influence on Dynamic and Cell Based Liver Tests during Extracorporeal Treatments

Bioartificial Therapy of Sepsis: Changes of Norepinephrine-Dosage in Patients and Influence on Dynamic and Cell Based Liver Tests during Extracorporeal Treatments

Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis–Treated Patients Using Stacked Generalization: Model Development and Validation Study

Leveraging electronic health record data to inform hospital resource management

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